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Energy Efficient Spectrum Aware Clustering for Cognitive Sensor Networks

Title:

Energy Efficient Spectrum Aware Clustering for Cognitive Sensor Networks

latiwesh, akrem (2015) Energy Efficient Spectrum Aware Clustering for Cognitive Sensor Networks. Masters thesis, Concordia University.

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Abstract

Combining cognitive radio technology with wireless sensor networks has been introduced in the literature as a solution to the spectrum deficiency problem. Many clustering algorithms have been proposed for wireless sensor networks. However, most of them are not suitable for cognitive sensor networks as they operate on a fixed channel settings. In this work, we propose a low energy spectrum aware clustering algorithm, CogLEACH-C, based on CogLEACH algorithm in order to improve the performance in terms of system lifetime.To accomplish that, energy level of each sensor node should be taken into account during the selection process. CogLEACH-C uses not only the number of channels sensed idle by the node but also the node energy level in determining the probability for each node to be a cluster head. Hence, allows the base station to select K cluster heads that are in a better position for the nodes in the network.

Moreover, as the network consists of primary users (PU) and secondary users sharing the same channels with the priority for the primary users, secondary nodes that are within the coverage of primary users will sense a low number of idle channels depending on the primary user’s activity. As a result, those nodes will experience difficulty to connect with a cluster head and that affects the
performance of the network.In particular, when the probability of a channel to be idle is low and the majority of the channels are within the coverage of the PUs. We provide an analysis of the probability of connectivity of a sensor node in cognitive wireless sensor network when CogLeach-C algorithm is used, and we show how that affects the performance of the network in terms of delay and network lifetime.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:latiwesh, akrem
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Date:17 April 2015
Thesis Supervisor(s):Qiu, Dongyu
ID Code:981127
Deposited By: AKREM LATIWESH
Deposited On:15 Jun 2016 16:10
Last Modified:18 Jan 2018 17:52
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